Overview

Dataset statistics

Number of variables18
Number of observations8950
Missing cells314
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory144.0 B

Variable types

Categorical1
Numeric17

Alerts

CUST_ID has a high cardinality: 8950 distinct valuesHigh cardinality
BALANCE is highly overall correlated with BALANCE_FREQUENCY and 4 other fieldsHigh correlation
BALANCE_FREQUENCY is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
PURCHASES is highly overall correlated with ONEOFF_PURCHASES and 5 other fieldsHigh correlation
ONEOFF_PURCHASES is highly overall correlated with PURCHASES and 2 other fieldsHigh correlation
INSTALLMENTS_PURCHASES is highly overall correlated with PURCHASES and 3 other fieldsHigh correlation
CASH_ADVANCE is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
PURCHASES_FREQUENCY is highly overall correlated with PURCHASES and 3 other fieldsHigh correlation
ONEOFF_PURCHASES_FREQUENCY is highly overall correlated with PURCHASES and 2 other fieldsHigh correlation
PURCHASES_INSTALLMENTS_FREQUENCY is highly overall correlated with PURCHASES and 3 other fieldsHigh correlation
CASH_ADVANCE_FREQUENCY is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
CASH_ADVANCE_TRX is highly overall correlated with BALANCE and 2 other fieldsHigh correlation
PURCHASES_TRX is highly overall correlated with PURCHASES and 5 other fieldsHigh correlation
MINIMUM_PAYMENTS is highly overall correlated with BALANCE and 1 other fieldsHigh correlation
MINIMUM_PAYMENTS has 313 (3.5%) missing valuesMissing
CUST_ID is uniformly distributedUniform
CUST_ID has unique valuesUnique
PURCHASES has 2044 (22.8%) zerosZeros
ONEOFF_PURCHASES has 4302 (48.1%) zerosZeros
INSTALLMENTS_PURCHASES has 3916 (43.8%) zerosZeros
CASH_ADVANCE has 4628 (51.7%) zerosZeros
PURCHASES_FREQUENCY has 2043 (22.8%) zerosZeros
ONEOFF_PURCHASES_FREQUENCY has 4302 (48.1%) zerosZeros
PURCHASES_INSTALLMENTS_FREQUENCY has 3915 (43.7%) zerosZeros
CASH_ADVANCE_FREQUENCY has 4628 (51.7%) zerosZeros
CASH_ADVANCE_TRX has 4628 (51.7%) zerosZeros
PURCHASES_TRX has 2044 (22.8%) zerosZeros
PAYMENTS has 240 (2.7%) zerosZeros
PRC_FULL_PAYMENT has 5903 (66.0%) zerosZeros

Reproduction

Analysis started2023-06-10 08:24:43.627685
Analysis finished2023-06-10 08:26:28.278395
Duration1 minute and 44.65 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

CUST_ID
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct8950
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size70.0 KiB
C10001
 
1
C16135
 
1
C16129
 
1
C16130
 
1
C16131
 
1
Other values (8945)
8945 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters53700
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8950 ?
Unique (%)100.0%

Sample

1st rowC10001
2nd rowC10002
3rd rowC10003
4th rowC10004
5th rowC10005

Common Values

ValueCountFrequency (%)
C10001 1
 
< 0.1%
C16135 1
 
< 0.1%
C16129 1
 
< 0.1%
C16130 1
 
< 0.1%
C16131 1
 
< 0.1%
C16132 1
 
< 0.1%
C16133 1
 
< 0.1%
C16134 1
 
< 0.1%
C16136 1
 
< 0.1%
C16144 1
 
< 0.1%
Other values (8940) 8940
99.9%

Length

2023-06-10T11:56:28.509076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c10001 1
 
< 0.1%
c10011 1
 
< 0.1%
c10017 1
 
< 0.1%
c10016 1
 
< 0.1%
c10015 1
 
< 0.1%
c10014 1
 
< 0.1%
c10013 1
 
< 0.1%
c10012 1
 
< 0.1%
c10010 1
 
< 0.1%
c10034 1
 
< 0.1%
Other values (8940) 8940
99.9%

Most occurring characters

ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44750
83.3%
Uppercase Letter 8950
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12672
28.3%
0 3737
 
8.4%
2 3652
 
8.2%
3 3651
 
8.2%
5 3642
 
8.1%
7 3640
 
8.1%
4 3636
 
8.1%
6 3633
 
8.1%
8 3633
 
8.1%
9 2854
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
C 8950
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 44750
83.3%
Latin 8950
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12672
28.3%
0 3737
 
8.4%
2 3652
 
8.2%
3 3651
 
8.2%
5 3642
 
8.1%
7 3640
 
8.1%
4 3636
 
8.1%
6 3633
 
8.1%
8 3633
 
8.1%
9 2854
 
6.4%
Latin
ValueCountFrequency (%)
C 8950
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12672
23.6%
C 8950
16.7%
0 3737
 
7.0%
2 3652
 
6.8%
3 3651
 
6.8%
5 3642
 
6.8%
7 3640
 
6.8%
4 3636
 
6.8%
6 3633
 
6.8%
8 3633
 
6.8%

BALANCE
Real number (ℝ)

Distinct8871
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1564.4748
Minimum0
Maximum19043.139
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:28.840591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.8145184
Q1128.28192
median873.38523
Q32054.14
95-th percentile5909.1118
Maximum19043.139
Range19043.139
Interquartile range (IQR)1925.8581

Descriptive statistics

Standard deviation2081.5319
Coefficient of variation (CV)1.3304988
Kurtosis7.6747513
Mean1564.4748
Median Absolute Deviation (MAD)799.8652
Skewness2.393386
Sum14002050
Variance4332775
MonotonicityNot monotonic
2023-06-10T11:56:29.200923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 80
 
0.9%
40.900749 1
 
< 0.1%
1213.551338 1
 
< 0.1%
1253.188317 1
 
< 0.1%
5058.299635 1
 
< 0.1%
296.905944 1
 
< 0.1%
1084.652647 1
 
< 0.1%
237.198442 1
 
< 0.1%
1636.518315 1
 
< 0.1%
468.851415 1
 
< 0.1%
Other values (8861) 8861
99.0%
ValueCountFrequency (%)
0 80
0.9%
0.000199 1
 
< 0.1%
0.001146 1
 
< 0.1%
0.001214 1
 
< 0.1%
0.001289 1
 
< 0.1%
0.004816 1
 
< 0.1%
0.006651 1
 
< 0.1%
0.009684 1
 
< 0.1%
0.01968 1
 
< 0.1%
0.021102 1
 
< 0.1%
ValueCountFrequency (%)
19043.13856 1
< 0.1%
18495.55855 1
< 0.1%
16304.88925 1
< 0.1%
16259.44857 1
< 0.1%
16115.5964 1
< 0.1%
15532.33972 1
< 0.1%
15258.2259 1
< 0.1%
15244.74865 1
< 0.1%
15155.53286 1
< 0.1%
14581.45914 1
< 0.1%

BALANCE_FREQUENCY
Real number (ℝ)

Distinct43
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87727073
Minimum0
Maximum1
Zeros80
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:29.900993image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.272727
Q10.888889
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.111111

Descriptive statistics

Standard deviation0.236904
Coefficient of variation (CV)0.27004663
Kurtosis3.0923696
Mean0.87727073
Median Absolute Deviation (MAD)0
Skewness-2.0232655
Sum7851.573
Variance0.056123506
MonotonicityNot monotonic
2023-06-10T11:56:30.251506image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.818182 278
 
3.1%
0.727273 223
 
2.5%
0.545455 219
 
2.4%
0.636364 209
 
2.3%
0.454545 172
 
1.9%
0.363636 170
 
1.9%
0.272727 151
 
1.7%
0.181818 146
 
1.6%
Other values (33) 761
 
8.5%
ValueCountFrequency (%)
0 80
0.9%
0.090909 67
0.7%
0.1 8
 
0.1%
0.111111 5
 
0.1%
0.125 9
 
0.1%
0.142857 7
 
0.1%
0.166667 7
 
0.1%
0.181818 146
1.6%
0.2 9
 
0.1%
0.222222 5
 
0.1%
ValueCountFrequency (%)
1 6211
69.4%
0.909091 410
 
4.6%
0.9 55
 
0.6%
0.888889 53
 
0.6%
0.875 57
 
0.6%
0.857143 51
 
0.6%
0.833333 60
 
0.7%
0.818182 278
 
3.1%
0.8 20
 
0.2%
0.777778 22
 
0.2%

PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6203
Distinct (%)69.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1003.2048
Minimum0
Maximum49039.57
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:30.616734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q139.635
median361.28
Q31110.13
95-th percentile3998.6195
Maximum49039.57
Range49039.57
Interquartile range (IQR)1070.495

Descriptive statistics

Standard deviation2136.6348
Coefficient of variation (CV)2.1298091
Kurtosis111.38877
Mean1003.2048
Median Absolute Deviation (MAD)361.28
Skewness8.1442691
Sum8978683.3
Variance4565208.2
MonotonicityNot monotonic
2023-06-10T11:56:30.994519image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
 
22.8%
45.65 27
 
0.3%
60 16
 
0.2%
150 16
 
0.2%
300 13
 
0.1%
200 13
 
0.1%
100 13
 
0.1%
450 12
 
0.1%
50 10
 
0.1%
600 10
 
0.1%
Other values (6193) 6776
75.7%
ValueCountFrequency (%)
0 2044
22.8%
0.01 4
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 2
 
< 0.1%
1.4 1
 
< 0.1%
2 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
ValueCountFrequency (%)
49039.57 1
< 0.1%
41050.4 1
< 0.1%
40040.71 1
< 0.1%
38902.71 1
< 0.1%
35131.16 1
< 0.1%
32539.78 1
< 0.1%
31299.35 1
< 0.1%
27957.68 1
< 0.1%
27790.42 1
< 0.1%
26784.62 1
< 0.1%

ONEOFF_PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4014
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean592.43737
Minimum0
Maximum40761.25
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:31.374802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38
Q3577.405
95-th percentile2671.094
Maximum40761.25
Range40761.25
Interquartile range (IQR)577.405

Descriptive statistics

Standard deviation1659.8879
Coefficient of variation (CV)2.8017948
Kurtosis164.18757
Mean592.43737
Median Absolute Deviation (MAD)38
Skewness10.045083
Sum5302314.5
Variance2755227.9
MonotonicityNot monotonic
2023-06-10T11:56:31.753544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4302
48.1%
45.65 46
 
0.5%
50 17
 
0.2%
200 15
 
0.2%
60 13
 
0.1%
100 13
 
0.1%
150 12
 
0.1%
70 12
 
0.1%
1000 12
 
0.1%
250 11
 
0.1%
Other values (4004) 4497
50.2%
ValueCountFrequency (%)
0 4302
48.1%
0.01 7
 
0.1%
0.02 2
 
< 0.1%
0.05 1
 
< 0.1%
0.24 1
 
< 0.1%
0.7 1
 
< 0.1%
1 4
 
< 0.1%
1.4 2
 
< 0.1%
2 1
 
< 0.1%
4.99 1
 
< 0.1%
ValueCountFrequency (%)
40761.25 1
< 0.1%
40624.06 1
< 0.1%
34087.73 1
< 0.1%
33803.84 1
< 0.1%
26547.43 1
< 0.1%
26514.32 1
< 0.1%
25122.77 1
< 0.1%
24543.52 1
< 0.1%
23032.97 1
< 0.1%
22257.39 1
< 0.1%

INSTALLMENTS_PURCHASES
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4452
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.06764
Minimum0
Maximum22500
Zeros3916
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:32.149829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median89
Q3468.6375
95-th percentile1750.0875
Maximum22500
Range22500
Interquartile range (IQR)468.6375

Descriptive statistics

Standard deviation904.33812
Coefficient of variation (CV)2.199974
Kurtosis96.575178
Mean411.06764
Median Absolute Deviation (MAD)89
Skewness7.2991199
Sum3679055.4
Variance817827.43
MonotonicityNot monotonic
2023-06-10T11:56:32.495910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3916
43.8%
300 14
 
0.2%
200 14
 
0.2%
100 14
 
0.2%
150 12
 
0.1%
125 11
 
0.1%
75 9
 
0.1%
350 8
 
0.1%
450 8
 
0.1%
500 8
 
0.1%
Other values (4442) 4936
55.2%
ValueCountFrequency (%)
0 3916
43.8%
1.95 1
 
< 0.1%
4.44 1
 
< 0.1%
4.8 1
 
< 0.1%
6.33 1
 
< 0.1%
7.26 1
 
< 0.1%
7.67 1
 
< 0.1%
9.28 1
 
< 0.1%
9.58 1
 
< 0.1%
9.65 1
 
< 0.1%
ValueCountFrequency (%)
22500 1
< 0.1%
15497.19 1
< 0.1%
14686.1 1
< 0.1%
13184.43 1
< 0.1%
12738.47 1
< 0.1%
12560.85 1
< 0.1%
12541 1
< 0.1%
12375 1
< 0.1%
12235.05 1
< 0.1%
12128.94 1
< 0.1%

CASH_ADVANCE
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4323
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean978.87111
Minimum0
Maximum47137.212
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:32.862127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31113.8211
95-th percentile4647.1691
Maximum47137.212
Range47137.212
Interquartile range (IQR)1113.8211

Descriptive statistics

Standard deviation2097.1639
Coefficient of variation (CV)2.1424311
Kurtosis52.899434
Mean978.87111
Median Absolute Deviation (MAD)0
Skewness5.1666091
Sum8760896.5
Variance4398096.3
MonotonicityNot monotonic
2023-06-10T11:56:33.230221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
495.425832 1
 
< 0.1%
1486.243293 1
 
< 0.1%
855.232779 1
 
< 0.1%
3767.104707 1
 
< 0.1%
291.608512 1
 
< 0.1%
38.690552 1
 
< 0.1%
521.664369 1
 
< 0.1%
1974.202963 1
 
< 0.1%
2462.100789 1
 
< 0.1%
Other values (4313) 4313
48.2%
ValueCountFrequency (%)
0 4628
51.7%
14.222216 1
 
< 0.1%
18.042768 1
 
< 0.1%
18.117967 1
 
< 0.1%
18.123413 1
 
< 0.1%
18.126683 1
 
< 0.1%
18.149946 1
 
< 0.1%
18.204577 1
 
< 0.1%
18.240626 1
 
< 0.1%
18.280043 1
 
< 0.1%
ValueCountFrequency (%)
47137.21176 1
< 0.1%
29282.10915 1
< 0.1%
27296.48576 1
< 0.1%
26268.69989 1
< 0.1%
26194.04954 1
< 0.1%
23130.82106 1
< 0.1%
22665.7785 1
< 0.1%
21943.84942 1
< 0.1%
20712.67008 1
< 0.1%
20277.33112 1
< 0.1%

PURCHASES_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49035055
Minimum0
Maximum1
Zeros2043
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:33.596308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.083333
median0.5
Q30.916667
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.833334

Descriptive statistics

Standard deviation0.40137075
Coefficient of variation (CV)0.81853839
Kurtosis-1.6386309
Mean0.49035055
Median Absolute Deviation (MAD)0.416667
Skewness0.060164236
Sum4388.6374
Variance0.16109848
MonotonicityNot monotonic
2023-06-10T11:56:33.975805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1 2178
24.3%
0 2043
22.8%
0.083333 677
 
7.6%
0.916667 396
 
4.4%
0.5 395
 
4.4%
0.166667 392
 
4.4%
0.833333 373
 
4.2%
0.333333 367
 
4.1%
0.25 345
 
3.9%
0.583333 316
 
3.5%
Other values (37) 1468
16.4%
ValueCountFrequency (%)
0 2043
22.8%
0.083333 677
 
7.6%
0.090909 43
 
0.5%
0.1 27
 
0.3%
0.111111 18
 
0.2%
0.125 32
 
0.4%
0.142857 26
 
0.3%
0.166667 392
 
4.4%
0.181818 16
 
0.2%
0.2 19
 
0.2%
ValueCountFrequency (%)
1 2178
24.3%
0.916667 396
 
4.4%
0.909091 28
 
0.3%
0.9 24
 
0.3%
0.888889 18
 
0.2%
0.875 26
 
0.3%
0.857143 25
 
0.3%
0.833333 373
 
4.2%
0.818182 21
 
0.2%
0.8 9
 
0.1%

ONEOFF_PURCHASES_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20245768
Minimum0
Maximum1
Zeros4302
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:34.343084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.083333
Q30.3
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.29833607
Coefficient of variation (CV)1.4735725
Kurtosis1.1618456
Mean0.20245768
Median Absolute Deviation (MAD)0.083333
Skewness1.5356128
Sum1811.9963
Variance0.089004408
MonotonicityNot monotonic
2023-06-10T11:56:34.706307image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.166667 592
 
6.6%
1 481
 
5.4%
0.25 418
 
4.7%
0.333333 355
 
4.0%
0.416667 244
 
2.7%
0.5 235
 
2.6%
0.583333 197
 
2.2%
0.666667 167
 
1.9%
Other values (37) 855
 
9.6%
ValueCountFrequency (%)
0 4302
48.1%
0.083333 1104
 
12.3%
0.090909 56
 
0.6%
0.1 39
 
0.4%
0.111111 26
 
0.3%
0.125 41
 
0.5%
0.142857 37
 
0.4%
0.166667 592
 
6.6%
0.181818 34
 
0.4%
0.2 27
 
0.3%
ValueCountFrequency (%)
1 481
5.4%
0.916667 151
 
1.7%
0.909091 4
 
< 0.1%
0.9 1
 
< 0.1%
0.888889 2
 
< 0.1%
0.875 6
 
0.1%
0.857143 1
 
< 0.1%
0.833333 120
 
1.3%
0.818182 10
 
0.1%
0.8 4
 
< 0.1%

PURCHASES_INSTALLMENTS_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36443734
Minimum0
Maximum1
Zeros3915
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:35.071936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.166667
Q30.75
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.39744778
Coefficient of variation (CV)1.0905792
Kurtosis-1.3986322
Mean0.36443734
Median Absolute Deviation (MAD)0.166667
Skewness0.50920116
Sum3261.7142
Variance0.15796474
MonotonicityNot monotonic
2023-06-10T11:56:35.436970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 3915
43.7%
1 1331
 
14.9%
0.416667 388
 
4.3%
0.916667 345
 
3.9%
0.833333 311
 
3.5%
0.5 310
 
3.5%
0.166667 305
 
3.4%
0.666667 292
 
3.3%
0.75 291
 
3.3%
0.083333 275
 
3.1%
Other values (37) 1187
 
13.3%
ValueCountFrequency (%)
0 3915
43.7%
0.083333 275
 
3.1%
0.090909 12
 
0.1%
0.1 6
 
0.1%
0.111111 9
 
0.1%
0.125 5
 
0.1%
0.142857 6
 
0.1%
0.166667 305
 
3.4%
0.181818 14
 
0.2%
0.2 9
 
0.1%
ValueCountFrequency (%)
1 1331
14.9%
0.916667 345
 
3.9%
0.909091 25
 
0.3%
0.9 19
 
0.2%
0.888889 28
 
0.3%
0.875 28
 
0.3%
0.857143 30
 
0.3%
0.833333 311
 
3.5%
0.818182 21
 
0.2%
0.8 18
 
0.2%

CASH_ADVANCE_FREQUENCY
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1351442
Minimum0
Maximum1.5
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:35.801672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.222222
95-th percentile0.583333
Maximum1.5
Range1.5
Interquartile range (IQR)0.222222

Descriptive statistics

Standard deviation0.20012139
Coefficient of variation (CV)1.4807989
Kurtosis3.3347343
Mean0.1351442
Median Absolute Deviation (MAD)0
Skewness1.8286863
Sum1209.5406
Variance0.04004857
MonotonicityNot monotonic
2023-06-10T11:56:36.216172image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.166667 759
 
8.5%
0.25 578
 
6.5%
0.333333 439
 
4.9%
0.416667 273
 
3.1%
0.5 215
 
2.4%
0.583333 142
 
1.6%
0.666667 125
 
1.4%
0.090909 70
 
0.8%
Other values (44) 700
 
7.8%
ValueCountFrequency (%)
0 4628
51.7%
0.083333 1021
 
11.4%
0.090909 70
 
0.8%
0.1 39
 
0.4%
0.111111 29
 
0.3%
0.125 47
 
0.5%
0.142857 49
 
0.5%
0.166667 759
 
8.5%
0.181818 42
 
0.5%
0.2 21
 
0.2%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.25 1
 
< 0.1%
1.166667 2
 
< 0.1%
1.142857 1
 
< 0.1%
1.125 1
 
< 0.1%
1.1 1
 
< 0.1%
1.090909 1
 
< 0.1%
1 25
0.3%
0.916667 27
0.3%
0.909091 3
 
< 0.1%

CASH_ADVANCE_TRX
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2488268
Minimum0
Maximum123
Zeros4628
Zeros (%)51.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:36.644029image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile15
Maximum123
Range123
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.8246467
Coefficient of variation (CV)2.1006496
Kurtosis61.646862
Mean3.2488268
Median Absolute Deviation (MAD)0
Skewness5.7212982
Sum29077
Variance46.575803
MonotonicityNot monotonic
2023-06-10T11:56:37.094191image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
10 150
 
1.7%
Other values (55) 915
 
10.2%
ValueCountFrequency (%)
0 4628
51.7%
1 887
 
9.9%
2 620
 
6.9%
3 436
 
4.9%
4 384
 
4.3%
5 308
 
3.4%
6 246
 
2.7%
7 205
 
2.3%
8 171
 
1.9%
9 111
 
1.2%
ValueCountFrequency (%)
123 3
< 0.1%
110 1
 
< 0.1%
107 1
 
< 0.1%
93 1
 
< 0.1%
80 1
 
< 0.1%
71 1
 
< 0.1%
69 1
 
< 0.1%
63 1
 
< 0.1%
62 3
< 0.1%
61 1
 
< 0.1%

PURCHASES_TRX
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct173
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.709832
Minimum0
Maximum358
Zeros2044
Zeros (%)22.8%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:37.801067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q317
95-th percentile57
Maximum358
Range358
Interquartile range (IQR)16

Descriptive statistics

Standard deviation24.857649
Coefficient of variation (CV)1.6898662
Kurtosis34.7931
Mean14.709832
Median Absolute Deviation (MAD)7
Skewness4.6306553
Sum131653
Variance617.90272
MonotonicityNot monotonic
2023-06-10T11:56:38.232486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
12 570
 
6.4%
2 379
 
4.2%
6 352
 
3.9%
3 314
 
3.5%
4 285
 
3.2%
7 275
 
3.1%
5 267
 
3.0%
8 267
 
3.0%
Other values (163) 3530
39.4%
ValueCountFrequency (%)
0 2044
22.8%
1 667
 
7.5%
2 379
 
4.2%
3 314
 
3.5%
4 285
 
3.2%
5 267
 
3.0%
6 352
 
3.9%
7 275
 
3.1%
8 267
 
3.0%
9 248
 
2.8%
ValueCountFrequency (%)
358 1
< 0.1%
347 1
< 0.1%
344 1
< 0.1%
309 1
< 0.1%
308 1
< 0.1%
298 1
< 0.1%
274 1
< 0.1%
273 1
< 0.1%
254 1
< 0.1%
248 2
< 0.1%

CREDIT_LIMIT
Real number (ℝ)

Distinct205
Distinct (%)2.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean4494.4495
Minimum50
Maximum30000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:38.626937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile1000
Q11600
median3000
Q36500
95-th percentile12000
Maximum30000
Range29950
Interquartile range (IQR)4900

Descriptive statistics

Standard deviation3638.8157
Coefficient of variation (CV)0.80962435
Kurtosis2.8366559
Mean4494.4495
Median Absolute Deviation (MAD)1800
Skewness1.522464
Sum40220828
Variance13240980
MonotonicityNot monotonic
2023-06-10T11:56:39.053115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3000 784
 
8.8%
1500 722
 
8.1%
1200 621
 
6.9%
1000 614
 
6.9%
2500 612
 
6.8%
4000 506
 
5.7%
6000 463
 
5.2%
5000 389
 
4.3%
2000 371
 
4.1%
7500 277
 
3.1%
Other values (195) 3590
40.1%
ValueCountFrequency (%)
50 1
 
< 0.1%
150 5
 
0.1%
200 3
 
< 0.1%
300 14
 
0.2%
400 3
 
< 0.1%
450 6
 
0.1%
500 121
1.4%
600 21
 
0.2%
650 1
 
< 0.1%
700 20
 
0.2%
ValueCountFrequency (%)
30000 2
 
< 0.1%
28000 1
 
< 0.1%
25000 1
 
< 0.1%
23000 2
 
< 0.1%
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21500 2
 
< 0.1%
21000 2
 
< 0.1%
20500 1
 
< 0.1%
20000 10
0.1%

PAYMENTS
Real number (ℝ)

Distinct8711
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1733.1439
Minimum0
Maximum50721.483
Zeros240
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:39.446790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.988924
Q1383.27617
median856.90155
Q31901.1343
95-th percentile6082.0906
Maximum50721.483
Range50721.483
Interquartile range (IQR)1517.8582

Descriptive statistics

Standard deviation2895.0638
Coefficient of variation (CV)1.6704117
Kurtosis54.770736
Mean1733.1439
Median Absolute Deviation (MAD)581.35146
Skewness5.9076198
Sum15511637
Variance8381394.2
MonotonicityNot monotonic
2023-06-10T11:56:39.845786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 240
 
2.7%
201.802084 1
 
< 0.1%
398.316441 1
 
< 0.1%
826.036748 1
 
< 0.1%
2571.573214 1
 
< 0.1%
1903.279643 1
 
< 0.1%
454.888506 1
 
< 0.1%
956.028747 1
 
< 0.1%
4560.77572 1
 
< 0.1%
1825.349955 1
 
< 0.1%
Other values (8701) 8701
97.2%
ValueCountFrequency (%)
0 240
2.7%
0.049513 1
 
< 0.1%
0.056466 1
 
< 0.1%
2.389583 1
 
< 0.1%
3.500505 1
 
< 0.1%
4.523555 1
 
< 0.1%
4.841543 1
 
< 0.1%
5.070726 1
 
< 0.1%
9.040017 1
 
< 0.1%
9.533313 1
 
< 0.1%
ValueCountFrequency (%)
50721.48336 1
< 0.1%
46930.59824 1
< 0.1%
40627.59524 1
< 0.1%
39461.9658 1
< 0.1%
39048.59762 1
< 0.1%
36066.75068 1
< 0.1%
35843.62593 1
< 0.1%
34107.07499 1
< 0.1%
33994.72785 1
< 0.1%
33486.31044 1
< 0.1%

MINIMUM_PAYMENTS
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8636
Distinct (%)> 99.9%
Missing313
Missing (%)3.5%
Infinite0
Infinite (%)0.0%
Mean864.20654
Minimum0.019163
Maximum76406.208
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:40.095140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.019163
5-th percentile73.282006
Q1169.12371
median312.34395
Q3825.48546
95-th percentile2766.5633
Maximum76406.208
Range76406.188
Interquartile range (IQR)656.36175

Descriptive statistics

Standard deviation2372.4466
Coefficient of variation (CV)2.745231
Kurtosis283.98999
Mean864.20654
Median Absolute Deviation (MAD)190.3741
Skewness13.622797
Sum7464151.9
Variance5628502.9
MonotonicityNot monotonic
2023-06-10T11:56:40.499682image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
299.351881 2
 
< 0.1%
150.317143 1
 
< 0.1%
271.528169 1
 
< 0.1%
6404.855484 1
 
< 0.1%
616.862544 1
 
< 0.1%
211.984193 1
 
< 0.1%
324.954747 1
 
< 0.1%
1600.26917 1
 
< 0.1%
277.546713 1
 
< 0.1%
342.28649 1
 
< 0.1%
Other values (8626) 8626
96.4%
(Missing) 313
 
3.5%
ValueCountFrequency (%)
0.019163 1
< 0.1%
0.037744 1
< 0.1%
0.05588 1
< 0.1%
0.059481 1
< 0.1%
0.117036 1
< 0.1%
0.261984 1
< 0.1%
0.311953 1
< 0.1%
0.319475 1
< 0.1%
1.113027 1
< 0.1%
1.334075 1
< 0.1%
ValueCountFrequency (%)
76406.20752 1
< 0.1%
61031.6186 1
< 0.1%
56370.04117 1
< 0.1%
50260.75947 1
< 0.1%
43132.72823 1
< 0.1%
42629.55117 1
< 0.1%
38512.12477 1
< 0.1%
31871.36379 1
< 0.1%
30528.4324 1
< 0.1%
29019.80288 1
< 0.1%

PRC_FULL_PAYMENT
Real number (ℝ)

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15371465
Minimum0
Maximum1
Zeros5903
Zeros (%)66.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:40.902774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.142857
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.142857

Descriptive statistics

Standard deviation0.2924992
Coefficient of variation (CV)1.9028713
Kurtosis2.4323953
Mean0.15371465
Median Absolute Deviation (MAD)0
Skewness1.9428199
Sum1375.7461
Variance0.08555578
MonotonicityNot monotonic
2023-06-10T11:56:41.308890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 5903
66.0%
1 488
 
5.5%
0.083333 426
 
4.8%
0.166667 166
 
1.9%
0.5 156
 
1.7%
0.25 156
 
1.7%
0.090909 153
 
1.7%
0.333333 134
 
1.5%
0.1 94
 
1.1%
0.2 83
 
0.9%
Other values (37) 1191
 
13.3%
ValueCountFrequency (%)
0 5903
66.0%
0.083333 426
 
4.8%
0.090909 153
 
1.7%
0.1 94
 
1.1%
0.111111 61
 
0.7%
0.125 52
 
0.6%
0.142857 54
 
0.6%
0.166667 166
 
1.9%
0.181818 75
 
0.8%
0.2 83
 
0.9%
ValueCountFrequency (%)
1 488
5.5%
0.916667 77
 
0.9%
0.909091 19
 
0.2%
0.9 16
 
0.2%
0.888889 12
 
0.1%
0.875 18
 
0.2%
0.857143 12
 
0.1%
0.833333 63
 
0.7%
0.818182 17
 
0.2%
0.8 33
 
0.4%

TENURE
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.517318
Minimum6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.0 KiB
2023-06-10T11:56:41.674649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile8
Q112
median12
Q312
95-th percentile12
Maximum12
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3383308
Coefficient of variation (CV)0.11620159
Kurtosis7.6948232
Mean11.517318
Median Absolute Deviation (MAD)0
Skewness-2.9430173
Sum103080
Variance1.7911292
MonotonicityNot monotonic
2023-06-10T11:56:41.947441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
6 204
 
2.3%
8 196
 
2.2%
7 190
 
2.1%
9 175
 
2.0%
ValueCountFrequency (%)
6 204
 
2.3%
7 190
 
2.1%
8 196
 
2.2%
9 175
 
2.0%
10 236
 
2.6%
11 365
 
4.1%
12 7584
84.7%
ValueCountFrequency (%)
12 7584
84.7%
11 365
 
4.1%
10 236
 
2.6%
9 175
 
2.0%
8 196
 
2.2%
7 190
 
2.1%
6 204
 
2.3%

Interactions

2023-06-10T11:56:21.229321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:46.087884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:51.977602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:58.010656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:03.680942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:10.023513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:15.616664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:21.376097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:27.087165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:33.481124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:39.786591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:46.096036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:51.618654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:57.708183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-06-10T11:55:25.400061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:31.260422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:37.909680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:44.028581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:50.125038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:55.975214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:01.755568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:07.560561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:13.472727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:19.559907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:25.306568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:50.596429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:56.350583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:02.338274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:08.282959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:14.289820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:19.809120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:25.751391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:31.653569image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:38.301746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:44.709189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:50.439256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:56.323238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:02.110934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:07.885486image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:13.812110image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:19.895911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:25.612225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:50.917883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:56.676111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:02.654035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:08.937377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:14.609940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:20.366120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:26.079796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:32.328153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:38.673279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:45.030020image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:50.764032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:56.659948image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:02.430684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:08.225861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:14.160436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:20.213556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:25.974550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:51.291179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:57.355726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:03.014780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:09.321208image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:14.962750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:20.720252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:26.432580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:32.728235image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:39.085897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:45.420120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:51.071810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:57.025206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:02.790893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:08.584210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:14.534599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:20.567183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:26.299842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:51.638705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:54:57.680061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:03.342362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:09.669256image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:15.280062image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:21.046301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:26.749147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:33.110887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:39.452982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:45.754832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:51.351582image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:55:57.366303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:03.117325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:08.901044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:14.872652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-06-10T11:56:20.895402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-06-10T11:56:42.318512image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
BALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
BALANCE1.0000.5450.0060.146-0.0900.566-0.1450.120-0.1440.5440.549-0.0460.3720.4320.900-0.4840.066
BALANCE_FREQUENCY0.5451.0000.1480.1350.1280.1370.2020.1590.1520.1770.1760.2030.1060.2070.502-0.1740.229
PURCHASES0.0060.1481.0000.7510.706-0.3850.7950.6930.606-0.391-0.3840.8850.2610.395-0.0080.2380.133
ONEOFF_PURCHASES0.1460.1350.7511.0000.200-0.1850.4240.9520.117-0.179-0.1750.5900.3050.3630.0700.0490.096
INSTALLMENTS_PURCHASES-0.0900.1280.7060.2001.000-0.3570.7860.1850.923-0.366-0.3570.7840.1230.239-0.0520.2760.125
CASH_ADVANCE0.5660.137-0.385-0.185-0.3571.000-0.454-0.189-0.3780.9410.952-0.4080.1630.2570.482-0.266-0.113
PURCHASES_FREQUENCY-0.1450.2020.7950.4240.786-0.4541.0000.4630.852-0.453-0.4470.9240.1040.172-0.1040.2920.098
ONEOFF_PURCHASES_FREQUENCY0.1200.1590.6930.9520.185-0.1890.4631.0000.112-0.176-0.1740.6070.2820.3210.0510.0610.084
PURCHASES_INSTALLMENTS_FREQUENCY-0.1440.1520.6060.1170.923-0.3780.8520.1121.000-0.382-0.3740.7810.0470.121-0.0850.2590.114
CASH_ADVANCE_FREQUENCY0.5440.177-0.391-0.179-0.3660.941-0.453-0.176-0.3821.0000.983-0.4070.0880.2030.456-0.287-0.131
CASH_ADVANCE_TRX0.5490.176-0.384-0.175-0.3570.952-0.447-0.174-0.3740.9831.000-0.3990.0970.2150.472-0.281-0.099
PURCHASES_TRX-0.0460.2030.8850.5900.784-0.4080.9240.6070.781-0.407-0.3991.0000.1900.284-0.0250.2530.169
CREDIT_LIMIT0.3720.1060.2610.3050.1230.1630.1040.2820.0470.0880.0970.1901.0000.4490.2640.0210.170
PAYMENTS0.4320.2070.3950.3630.2390.2570.1720.3210.1210.2030.2150.2840.4491.0000.3680.1870.205
MINIMUM_PAYMENTS0.9000.502-0.0080.070-0.0520.482-0.1040.051-0.0850.4560.472-0.0250.2640.3681.000-0.4780.137
PRC_FULL_PAYMENT-0.484-0.1740.2380.0490.276-0.2660.2920.0610.259-0.287-0.2810.2530.0210.187-0.4781.0000.020
TENURE0.0660.2290.1330.0960.125-0.1130.0980.0840.114-0.131-0.0990.1690.1700.2050.1370.0201.000

Missing values

2023-06-10T11:56:26.787409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-10T11:56:27.518198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-10T11:56:28.081175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
0C1000140.9007490.81818295.400.0095.400.0000000.1666670.0000000.0833330.000000021000.0201.802084139.5097870.00000012
1C100023202.4674160.9090910.000.000.006442.9454830.0000000.0000000.0000000.250000407000.04103.0325971072.3402170.22222212
2C100032495.1488621.000000773.17773.170.000.0000001.0000001.0000000.0000000.0000000127500.0622.066742627.2847870.00000012
3C100041666.6705420.6363641499.001499.000.00205.7880170.0833330.0833330.0000000.083333117500.00.000000NaN0.00000012
4C10005817.7143351.00000016.0016.000.000.0000000.0833330.0833330.0000000.000000011200.0678.334763244.7912370.00000012
5C100061809.8287511.0000001333.280.001333.280.0000000.6666670.0000000.5833330.000000081800.01400.0577702407.2460350.00000012
6C10007627.2608061.0000007091.016402.63688.380.0000001.0000001.0000001.0000000.00000006413500.06354.314328198.0658941.00000012
7C100081823.6527431.000000436.200.00436.200.0000001.0000000.0000001.0000000.0000000122300.0679.065082532.0339900.00000012
8C100091014.9264731.000000861.49661.49200.000.0000000.3333330.0833330.2500000.000000057000.0688.278568311.9634090.00000012
9C10010152.2259750.5454551281.601281.600.000.0000000.1666670.1666670.0000000.0000000311000.01164.770591100.3022620.00000012
CUST_IDBALANCEBALANCE_FREQUENCYPURCHASESONEOFF_PURCHASESINSTALLMENTS_PURCHASESCASH_ADVANCEPURCHASES_FREQUENCYONEOFF_PURCHASES_FREQUENCYPURCHASES_INSTALLMENTS_FREQUENCYCASH_ADVANCE_FREQUENCYCASH_ADVANCE_TRXPURCHASES_TRXCREDIT_LIMITPAYMENTSMINIMUM_PAYMENTSPRC_FULL_PAYMENTTENURE
8940C19181130.8385541.000000591.240.00591.240.0000001.0000000.0000000.8333330.000000061000.0475.52326282.7713201.006
8941C191825967.4752700.833333214.550.00214.558555.4093260.8333330.0000000.6666670.6666671359000.0966.202912861.9499060.006
8942C1918340.8297491.000000113.280.00113.280.0000001.0000000.0000000.8333330.000000061000.094.48882886.2831010.256
8943C191845.8717120.50000020.9020.900.000.0000000.1666670.1666670.0000000.00000001500.058.64488343.4737170.006
8944C19185193.5717220.8333331012.731012.730.000.0000000.3333330.3333330.0000000.000000024000.00.000000NaN0.006
8945C1918628.4935171.000000291.120.00291.120.0000001.0000000.0000000.8333330.000000061000.0325.59446248.8863650.506
8946C1918719.1832151.000000300.000.00300.000.0000001.0000000.0000000.8333330.000000061000.0275.861322NaN0.006
8947C1918823.3986730.833333144.400.00144.400.0000000.8333330.0000000.6666670.000000051000.081.27077582.4183690.256
8948C1918913.4575640.8333330.000.000.0036.5587780.0000000.0000000.0000000.16666720500.052.54995955.7556280.256
8949C19190372.7080750.6666671093.251093.250.00127.0400080.6666670.6666670.0000000.3333332231200.063.16540488.2889560.006